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(A ) Numbers of all face-to-face appointments of patients aged ≥75 years to GPs per 1000 person-years. The line shows the mean, and the dashed area the 95% confidence interval. ( B ) Numbers of non-urgent scheduled and urgent scheduled appointments to GPs per 1000 person-years. The appointments were derived from 4-knot-restricted cubic <t>spline</t> <t>regression</t> models. Grey areas represent 95% confidence intervals. pyrs = person-years; 0 = timepoint of <t>model</t> change.
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Odds ratios of smoking outcomes by level of implementation fidelity modelled from restricted cubic spline regression analysis

Journal: Prevention Science

Article Title: The Impact of Implementation Fidelity of a School-Based Multi-Component Smoking Prevention Intervention on Vocational Students’ Smoking Behavior: A Cluster-Randomized Controlled Trial

doi: 10.1007/s11121-024-01712-8

Figure Lengend Snippet: Odds ratios of smoking outcomes by level of implementation fidelity modelled from restricted cubic spline regression analysis

Article Snippet: As the cut-points for low, medium, and high implementation based on tertiles were arbitrary, we supplemented the models with graphic representations of fully adjusted restricted cubic spline regression models performed in RStudio (R Core Team, ) to be able to analyze all data points of the continuous variable and their relations to the outcome variables (Gauthier et al., ).

Techniques:

(A ) Numbers of all face-to-face appointments of patients aged ≥75 years to GPs per 1000 person-years. The line shows the mean, and the dashed area the 95% confidence interval. ( B ) Numbers of non-urgent scheduled and urgent scheduled appointments to GPs per 1000 person-years. The appointments were derived from 4-knot-restricted cubic spline regression models. Grey areas represent 95% confidence intervals. pyrs = person-years; 0 = timepoint of model change.

Journal: BJGP Open

Article Title: Comparison of two GP service provider models in older adults: a register-based follow-up study

doi: 10.3399/BJGPO.2022.0101

Figure Lengend Snippet: (A ) Numbers of all face-to-face appointments of patients aged ≥75 years to GPs per 1000 person-years. The line shows the mean, and the dashed area the 95% confidence interval. ( B ) Numbers of non-urgent scheduled and urgent scheduled appointments to GPs per 1000 person-years. The appointments were derived from 4-knot-restricted cubic spline regression models. Grey areas represent 95% confidence intervals. pyrs = person-years; 0 = timepoint of model change.

Article Snippet: A possible non-linear relationship between the number of contacts and the years was assessed by using a 4-knot-restricted cubic spline Poisson regression model. Stata (version 17.0) statistical package was used for the analysis.

Techniques: Derivative Assay

The top figure illustrates the numbers of all face-to-face appointments to GPs per 1000 person-years and the bottom figure the respective amount of GP workforce expressed as person-years adjusted to the number of patients aged ≥75 years. The appointments were derived from 4-knot-restricted cubic spline regression models. Grey areas represent 95% confidence intervals. pyrs = person-years; 0 = timepoint of model change.

Journal: BJGP Open

Article Title: Comparison of two GP service provider models in older adults: a register-based follow-up study

doi: 10.3399/BJGPO.2022.0101

Figure Lengend Snippet: The top figure illustrates the numbers of all face-to-face appointments to GPs per 1000 person-years and the bottom figure the respective amount of GP workforce expressed as person-years adjusted to the number of patients aged ≥75 years. The appointments were derived from 4-knot-restricted cubic spline regression models. Grey areas represent 95% confidence intervals. pyrs = person-years; 0 = timepoint of model change.

Article Snippet: A possible non-linear relationship between the number of contacts and the years was assessed by using a 4-knot-restricted cubic spline Poisson regression model. Stata (version 17.0) statistical package was used for the analysis.

Techniques: Derivative Assay